Self-adaptive analysis scale determination for terrain features in seafloor substrate classification. (5th June 2021) - Shang, Xiaodong; Robert, Katleen; Misiuk, Benjamin; Mackin-McLaughlin, Julia; Zhao, Jianhu
Abstract: Seafloor substrate mapping has become increasingly important to guide the management of marine ecosystems. Full coverage substrate maps, however, cannot easily be created from point samples (e.g. grabs, videos) as a result of the time required for collection and their discrete spatial extent. Instead, relationships between substrate types and surrogate variables as obtained from bathymetric or backscatter data can be modelled to build predictive substrate maps. As calculation of these surrogate variables is scale-dependent, the scale(s) of analysis need(s) to be selected first, with multiple scales likely required to adequately capture substrate characteristics. This paper proposes an objective and automatic self-adaptive analysis scale determination approach at each bathymetric point to extract terrain features (e.g. slope, aspect, etc). Object-based image analysis (OBIA) is also used to calculate additional texture features for segmented backscatter image objects. Random Forest classification is then used to model the relationship between these extracted features and substrate types interpreted from ground-truth video data, and full-coverage seafloor substrate maps are produced. The proposed method was applied on two datasets from Newfoundland, Canada, and demonstrated good performance in terms of both overall (>80%) and per-class accuracies. The proposed method is easily transferable to other study areas and provides an objective, repeatable means for classifying seafloor substrates for environmental protection and management of marine habitats. Highlights: An objective scale determination approach is proposed to derive terrain features. Principal Components Analysis is used to extract principle seafloor information. Entropy is calculated to reflect the seafloor orderliness. Polynomial methods are used to extract terrain attributes. The proposed method achieved good performance in two study areas.